Predictive Analytics Project in Automotive Industry

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Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.

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Predictive Analytics Project in Automotive Industry

  1. 1. Predictive Vehicle Inspection Matous Havlena matous@havlena.net Tim Ojo timmyojo@gmail.com Akin Alao alaoraufu@yahoo.co.uk
  2. 2. Project Charter Evaluate the feasibility of using Big Data analytics solutions for Manufacturing to solve the problem of Predictive Vehicle Inspection: ● Analyzing vehicle production history to predict car inspection failures from the production line. ● Production shifts, specific employee, and other factors The two Big Data Analytics solutions to be evaluated: ● IBM BigInsights ● Datameer 2.1
  3. 3. Approach & Proposed Solution ● Recognized the problem as a classification problem similar to credit scoring or fraud detection. ● Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. ● Build a predictive model based on machine learning classification (supervised learning) to identify whether a vehicle can be classified as good (passes quality check on 1st try) or bad (fails quality check on 1st try)
  4. 4. Proposed Solutions - Tools ● BigInsights + SPSS Modeler ○ Hadoop is used to store big data and execute data processing jobs in an efficient and distributed fashion. IBM provides BigInsights as a management and operational interface to simplify working with Hadoop without doing much coding. ○ SPSS Modeler is a data analytics workbench that allows the user to build predictive models by leveraging built in algorithms and functions without the need for programming
  5. 5. Proposed Solutions - Tools ● Datameer ○ Like BigInsights, Datameer Analytics Solution presents a web based spreadsheet interface on top of a Hadoop cluster and provides analytics functions and visualizations out of the box without the need for writing code. ○ DAS also has a Smart Analytics suite. One of the tools available in that suite is a decision tree model which is a descriptive model that can identify important factors that affect quality. ○ Datameer can also be extended to run predictive models created in R, SAS, SPSS, etc.
  6. 6. IBM Solution Architecture SPSS Modeler Client (only Windows) SPSS Modeler Server (multiplatform) SPSS Analytic Server ● allows analysts to do predictive analytics over big data ● data centric architecture ensures scalability and performance SPSS Analytic Catalyst ● automatically discovers statistically interesting relationships in data ● close the analytic specialist gap ● good in early discovery dataset stage (helps to focus on important parts) ● automate some parts of CRISP-DM SPSS Analytic Server (multiplatform) SPSS Analytic Catalyst Hadoop (BigInsights)
  7. 7. Prediction in SPSS Modeler 425 predictors 85.4% accuracy (on the training dataset)
  8. 8. Model Outcome Original value | Predicted value | Confidence
  9. 9. Predictor Importance
  10. 10. c5.0 Algorithm ● C5.o is an algorithm used to generate a decision tree which can be used for classification therefore it is often referred to as a statistical classifier ● A C5.0 model works by splitting the sample based on the field that provides the maximum information gain. Each subsample defined by the first split is then split again, usually based on a different field, and the process repeats until the subsamples cannot be split any further. Finally, the lowest-level splits are reexamined, and those that do not contribute significantly to the value of the model are removed or pruned.
  11. 11. c5.0 Algorithm ● C5.0 models are quite robust in the presence of problems such as missing data and large numbers of input fields. ● They usually do not require long training times to create. Because of the algorithm’s recursive nature it can benefit from parallel processing. ● C5.0 offers the boosting method to increase accuracy of classification
  12. 12. Datameer Analysis ● As previously mentioned Datameer has some built in advanced analytics tools but most of them are in the descriptive analytics area. The sole predictive analytics tool they have is a specialized recommendation engine. ● Datameer can be extended to include predictive models generated in tools like R, SAS, SPSS, etc. These take the form of functions in DAS similar to the concept of functions in Excel. ○ The disadvantage of this approach is that the hard work of building the model is done without the support of big data ○ Another disadvantage is the lack of tight integration that is present in the IBM solution however you do get the freedom to use any tool
  13. 13. Project Challenges & Opportunities ● Data understanding and formatting ● Time constraints ● More interaction with people on the ground ● More predictor data (diverse dataset is a key!) ○ Plant environment (temperature, humidity, pressure) ○ Specific employees ○ Supplier & parts data ○ Warranty data
  14. 14. Questions? Matous Havlena matous@havlena.net Tim Ojo timmyojo@gmail.com Akin Alao alaoraufu@yahoo.co.uk

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